None these “minor” things were approved by you. Yet, all of them are owned by you.

That is the shape of every story below. A platform toggle nobody saw. A terms update that landed on a Tuesday. An agent given access to a system by someone who has never spoken to a customer. None of these are model problems. They are permission problems, and permission is now the layer where marketing risk actually lives.

The screenshot that goes viral has your logo on it, not the vendor’s.

So the real question has changed. Not what your AI can do. What it can reach, and who let it.

In other words: if nobody asks you, are you still in control?

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Meta finally has a serious image model

If you had Meta written off as the company that somehow owns Instagram, WhatsApp, Facebook, half the world’s attention, and still couldn’t quite ship a frontier AI product, fair enough.

That view got harder to defend this week.

Meta just launched Muse Image, its new in-house image generation and editing model from Meta Superintelligence Labs, and previewed Muse Video. Muse Image is already rolling out across Meta AI, Instagram Stories in the US, and WhatsApp in select countries, with Facebook and Messenger coming later. It can generate images from prompts, edit existing images, compose scenes from multiple references, and use Instagram context when creating social images.

The interesting bit is that Muse Image is more than a prettier Midjourney clone. Meta says it can use search and coding tools before generating an image, which means it can reason through certain prompts, create charts or QR codes, and work alongside Muse Spark to produce things like GIFs, simple websites, and interactive visual experiences. Gemini had this feature first, and it makes a huge difference in image quality.

For us marketers, the real deal is Advantage+ Creative. Meta is not launching this as a cute consumer toy and hoping advertisers notice. It is pushing Muse directly into the ad machine, where creative variation, automated testing, and format-native production already matter.

If Muse gets good enough, “make me 40 variants for this audience, in this placement, with this product angle” becomes less of a production request and more of a default button.

And Meta, inconveniently for its competitors, already owns the button.

But the privacy problem is not a footnote

So, yes, Muse Image looks impressive. But the Instagram part of this launch is where the room gets colder.

According to reports from The New York Times, BBC, and Business Insider, Meta’s new AI image tools can use public Instagram photos and profile images as reference material when someone tags an account in a prompt. Adult public accounts are opted in by default unless they turn off the new “Sharing and Reuse” settings. Private accounts and under-18 users are excluded from this specific feature by default.

That is a very big deal.

If your brand has executives, creators, employees, ambassadors, customers, event photos, UGC, or public campaign assets on Instagram, this is a reputational-risk story. Someone can potentially generate images using public-facing people or content connected to your brand, and the account owner may not be notified when it happens.

To opt out while staying public, users need to go into Instagram settings, find “Sharing and Reuse,” and turn off the toggles that allow posts and reels to be reused on Instagram and with AI features at Meta. Making the account private is the stronger barrier, but obviously that is not practical for most professional profiles or brand accounts.

There is a strange tension here. Meta is making one of the strongest cases yet for social-native AI creativity. It is also making one of the strongest cases for why every marketing team needs a boring, grown-up AI governance checklist.

Check your brand accounts. Check your executives. Check your creators.

The fun feature and the privacy landmine are the same product.

The AI layoff story is getting messier

If you spent the last year hearing executives talk as if AI layoffs were just brave efficiency, you may enjoy this part.

Some of them are now hiring people back.

CNBC reported that companies including Ford, IBM, Commonwealth Bank of Australia, and Klarna have started reversing or softening AI-driven cuts after discovering that automation did not handle the messy parts of work as well as promised. Ford reportedly brought back hundreds of veteran engineers after automated systems missed quality problems. Commonwealth Bank reversed customer-service layoffs after its AI voice bot failed under real-world call volume and complexity. IBM automated a huge chunk of routine HR work, then ran straight into the 6% that required judgment, ethics, and actual humans.

The numbers are ugly in a useful way.

Orgvue found that among business leaders who made redundancies because of AI deployment, 55% later admitted some of those decisions were wrong. Robert Half found that about a third of US hiring managers had eliminated a role primarily because of AI and later rehired for the same or a similar role.

I’ve seen people read this as proof that AI replacement does not work. That is too comforting.

The better read is that lazy restructuring does not work. If you cut the people who understand the process, then ask AI to run the process, congratulations: you have automated the org chart your best people were quietly holding together with duct tape.

This is exactly why we built the Teamless workshop. The point is not “replace your team with AI.” That is how you end up in the CNBC article. The point is to understand which parts of the team should become systems, which parts require human judgment, and how the whole operating model changes when one good person can now orchestrate much more than before.

AI restructuring is real. So is AI restructuring theater.

Try very hard to do the first one.

HubSpot found the third rail of AI SaaS

There are few sentences more dangerous in SaaS than “we’ve updated our terms.”

HubSpot just proved it.

On July 1, HubSpot rolled out terms that would have allowed certain CRM enrichment data to be pooled into a shared commercial dataset for a new prospecting product. The company described the data as “business card-level” information: names, titles, companies, work emails, employer details, deliverability signals, and similar enrichment fields. HubSpot said core CRM records like notes, deals, call recordings, custom fields, and customer records were out of scope.

Customers heard something else: “Thanks for building a CRM database for years. We may now use pieces of it to improve a product everyone else can use.”

That gap killed the rollout. The default was opt-out, the communication felt legalistic, and the value exchange was not obvious. Within four days, after a very loud LinkedIn revolt, HubSpot reversed the change. Duncan Lennox, HubSpot’s chief product and technology officer, wrote that they “got this wrong” and said any future enrichment capability using customer data would be transparently opt-in.

To be fair, the underlying product idea is not stupid. A shared enrichment network could make prospecting data fresher and less terrible. Anyone who has worked with B2B contact data knows half of it ages like milk in a hot car.

But data pooling only works when customers feel they are joining a co-op, not being harvested by a vendor they already pay.

This is the warning for every marketing tool provider now racing to add AI: your customers’ data is not your growth hack. Their CRM, email engagement, support transcripts, ad performance, customer feedback, call notes, and campaign history are operational assets. Maybe they will share some of it with you. Maybe they will even be happy to, if the value is clear.

But the price tag has to be visible.

And “we buried it in the settings” is not a consent strategy.

Your AI rollout is probably already a security problem

A lot of companies are deploying AI the way teenagers install browser extensions.

Click. Allow. Hope nothing weird happens.

DigiCert’s new AI Trust Outlook suggests the bill is coming due. In a survey of 1,001 IT and cybersecurity decision-makers across the US, UK, and Australia, 78% of organizations said they had either experienced an AI-related security incident or identified an AI-related vulnerability. Around half of respondents had at least one incident involving an unauthorized or misconfigured AI agent.

That matters because the risk is not just “someone pasted confidential data into ChatGPT.” That is still bad, obviously. But agents are different. They can access systems, trigger workflows, retrieve documents, send messages, and make decisions at machine speed.

A badly configured chatbot is annoying. A badly configured agent with CRM access is a very expensive intern with no fear and too many permissions.

For us marketers, the support chatbot is the obvious danger zone. It has access to product documentation, customer conversations, internal escalation rules, maybe even account data. Can it be prompt-injected into revealing things it should not reveal? Can it be tricked into inventing refund policies? Can it leak roadmap details? Can it hand over competitor-sensitive information because someone asked nicely in the right format?

This is not just an IT problem. The PR disaster will have your logo on it.

So before you launch another AI layer into customer support, lead gen, social care, or sales enablement, ask the dull questions. What can it access? Who approved that access? Can we revoke it quickly? Do we log what it does? Can we trace why it gave an answer? Has anyone tried to break it before customers do?

The boring questions are now the brand-safety questions.

And some companies will only learn that after the chatbot screenshots hit LinkedIn.

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The floor came up. Teams that used to ship bad work now ship competent work. But the ceiling came down with it, because the teams that used to make something remarkable saw the shortcut to competence and took it. So now everyone serves the same vanilla, and buyers can’t tell anyone apart.

Olly watches this as a commercial operator, not a marketer. He spent 10+ years at American Express running $3B+ global accounts, founded and exited a tech services company, and now works as a fractional CRO across 15+ startups a year — while running the Forward Deployed Marketer division at Myosin. His lens is revenue, which means he tracks the number one AI marketing content skip: what all that production actually costs to turn into a customer.

We sent him our questions. His answers are below, lightly edited as always.

You spent a decade at American Express running $3B+ global accounts, then built and exited a company. What did closing enterprise deals at that scale teach you that most AI-era marketing advice gets wrong?

At AmEx, I learned fast that you could show a VP how to save their company millions of dollars, and they still might not move on it — because the decision didn’t help their career. Maybe it made another department look good. Maybe it created political risk. Maybe it didn’t align with what their boss cared about that quarter.

You have to understand what that individual person needs to succeed, not just what the company needs.

Most AI-era marketing advice treats buyers like rational economic actors. Here’s the ROI. Here are the cost savings. Here’s the efficiency gain. But enterprise deals don’t close on spreadsheets. They close on trust, internal politics, and whether the buyer can take credit for the win.

We’ve also lost the face-to-face context post-COVID. Before, I’d meet stakeholders in person every week. You’d pick up on body language, side conversations, who deferred to whom in the room. You’d understand what someone really wanted versus what they said on the call.

Now everything’s remote, and you lose that context. Standard AI tools make it worse — they optimize for volume and efficiency, not for reading the room or understanding the human on the other side.

The companies winning enterprise deals today are the ones that still prioritize the individual buyer’s agenda, not just the company’s problem.

You’ve said most AI gives teams 70th-percentile output, and your work is helping them hold 95th-percentile thinking at scale. What’s the actual difference in practice — and where does a smart team accept the 70th percentile without realizing it?

AI can write copy. It can summarize data. It can generate outreach sequences. But it struggles with why this matters to this specific person.

70th percentile positioning sounds fine on the surface. It’s grammatically correct. It uses the right keywords. It checks the boxes. But it’s generic. It tries to appeal to everyone, so it resonates with no one.

95th percentile positioning is sharp. It’s specific. It knows exactly who it’s for and what problem it’s solving. It doesn’t hedge. It doesn’t try to be all things to all people.

My favorite analogy is ice cream. A cheap knockoff Cornetto, three scoops of vanilla in a cup, and an extravagant sundae are all technically ice cream. But they’re not the same.

AI raised the floor: people who used to sell crappy fake Cornettos can now produce decent scoops of vanilla. That’s progress. But AI also lowered the ceiling: the companies that used to make beautiful, differentiated, memorable sundaes started thinking, “Why put in all that effort when we can serve vanilla scoops like everyone else? It’s faster, cheaper, and people still buy it.”

Now everyone’s selling vanilla ice cream in a cup. Things got dull. Hard to differentiate. And the buyers are overwhelmed by sameness.

The trap: AI makes it easy to produce “good enough” content at scale. But good enough isn’t remarkable. It doesn’t stand out. It doesn’t get shared. It doesn’t close deals.

The opportunity: the companies still making sundaes — the ones investing in positioning, storytelling, and depth — are the ones people remember. AI didn’t kill differentiation. It just made mediocrity more accessible.

You help companies use AI without falling into the traps everyone else does. Name the three you see most often from teams that look sophisticated from the outside but aren’t actually working.

Trap 1: Bolting AI onto broken processes. Most companies add AI to systems that already don’t work. They automate bad workflows and make them faster. Your sales process is unclear, your ICP is fuzzy, your messaging doesn’t resonate — so you add AI to scale outreach. Now you’re sending 10x more emails that still don’t convert and are potentially degrading your brand. AI didn’t fix the problem. It amplified it.

Trap 2: Optimizing for volume over quality. AI makes it easy to produce more — more content, more outreach, more campaigns. So teams do. But nobody’s asking whether they should be producing this in the first place. I see companies publishing 20 blog posts a month that nobody reads, sending 5,000 outreach emails with 0.2% reply rates, running campaigns that generate leads but not revenue. Volume feels like progress. But if the output doesn’t move the business forward, it’s just noise.

Trap 3: Focusing on how content is produced, not how it’s received. This is the one I see most right now. Teams obsess over tools, workflows, and prompts. How can we produce this faster? How can we automate it? But they’re not asking: if I were on the receiving end of this, would I care? There are hundreds of tools built to help people produce AI content. Almost none focused on how people receive it and use it to make decisions. If your AI-generated email looks like every other AI-generated email, the recipient deletes it. Before you hit send, ask: if I received this, would I actually read it? Would it change my behavior? If the answer is no, don’t send it — it doesn’t matter how efficiently you produced it.

A lot of AI marketing content chases output volume — more content, more emails, more outreach. As a commercial operator, where does that lead to the worst outcome for a CMO, and what metric actually captures the damage?

The worst commercial outcome: burning through your contact list without understanding cost per acquisition. I’ve seen companies send hundreds of thousands of outreach emails without ever looking at their CAC. They’re optimizing for activity — leads generated, emails sent, content published — instead of outcomes. And by the time they realize the economics don’t work, they’ve burned their reputation with their target market.

The metric that catches the damage is cost per acquisition. Most companies track top-of-funnel metrics: open rates, click rates, leads generated. But they don’t track how much it actually costs to acquire a customer. If your CAC is climbing while your volume increases, something’s broken — you’re reaching more people, but the quality is dropping, your messaging isn’t resonating, or you’re attracting the wrong audience.

The other metric I watch is reply sentiment. Not just reply rate, but what people actually say when they reply. “Not interested” or “unsubscribe” at scale means your messaging is off. “This doesn’t apply to us” means your targeting is wrong.

There’s also an opportunity to improve at allocating tokens to specific initiatives and measuring ROI from that allocation. Right now, most companies have AI running in the background, racking up costs without understanding what they’re getting for it. It’s like leaving the lights on in every room of the house. Start tracking which AI workflows generate revenue and which generate noise, and allocate accordingly.

Pricing is one of the hardest things for B2B founders past PMF, and AI is making it messier—value-based, usage-based, and AI feature add-ons. What pricing mistake do you see most often right now, and what’s a simple test you’d run before changing a model?

The biggest mistake: trying to extrapolate SMB pricing into an enterprise model. I see this constantly. A company has a $150/month SaaS product for individuals or small businesses. It works. Then an enterprise buyer shows interest, and instead of rethinking the model entirely, they just scale it up. “$150/month × 100 seats = $15K/month enterprise plan.”

But enterprise buyers aren’t buying seats anymore. They’re buying outcomes, services, integrations, workshops, and support. AI brings too many personalization opportunities for seat-based pricing to hold. Your product might be part of the solution, but it’s not the whole solution. If you’re still charging per seat when the enterprise buyer is thinking about business outcomes, you’re creating confusion, not clarity.

The simple test I run before changing a pricing model: put yourself in the buyer’s shoes and imagine explaining the purchase to their boss. If the buyer struggles to explain the value clearly, or the justification feels weak, your pricing model is wrong. Good pricing makes the buyer’s internal pitch easy and makes them look like a star. Bad pricing makes them do mental gymnastics to justify it.

“Forward-deployed AI marketing” is a sharp phrase. What does it actually look like in execution for a B2B company past PMF — the team, the tooling, and week one?

Forward-deployed means we’re embedded, not advising from the outside. A typical setup is two FDM operators — we find that works much better than one or three-plus builders — plus an account manager so the operators focus on building, not project admin, and internal stakeholders on the client side: a CMO or growth lead and a sales lead. Weekly sync, daily async.

We don’t bring a pre-set stack. We work with what they have and add only what’s missing. Common tools in the mix: Clay for data enrichment and outbound; Claude or GPT for content and research synthesis; Relay or Make for workflow automation; and their existing CRM—Salesforce, HubSpot, Pipedrive, whatever they use. The philosophy is to build systems they own, using tools they can run after we leave. We’re not creating a dependency. We’re building infrastructure.

Week one starts with a tech stack audit. Most companies have AI-native tools like Clay or traditional SaaS with new AI capabilities like Salesforce that are massively underutilized. We map what they have, identify what’s being ignored, and almost always find quick wins — honestly, often around meeting transcripts, internal and external, that nobody’s reviewing. There’s incredible signal in those: customer pain points, competitive intel, positioning feedback, objection patterns. Most teams record the meeting, file it, and never look at it again.

Common week-one deliverables: tech stack rationalization (what you have, what you’re using, what’s wasted), a data source audit (especially underused sources like transcripts, CRM notes, support tickets), and quick wins we can surface in days, not months.

You’ve said traditional agencies put their A-team on the pitch and juniors on the work. With AI layered on top, what should a senior marketer evaluate differently when hiring an agency in 2026 — and what question are they not asking?

The question they’re not asking: are you using AI as leverage or as a replacement? Most agencies use AI to replace junior labor — automating the work that used to go to entry-level marketers: content writing, social scheduling, basic research. That’s fine if you’re optimizing for cost. It’s terrible if you’re optimizing for quality. AI produces 70th-percentile output by default. If you use it to replace humans without adding a judgment layer, you get average work at scale.

What senior marketers should evaluate in 2026: Who’s actually doing the work — not just who’s on the team, but who’s reviewing the AI output, making the strategic calls, ensuring quality doesn’t collapse? What’s their AI operating model — using AI to multiply senior capacity (good), or to eliminate junior capacity without replacing it with senior judgment (bad)? What’s their quality control process — how do they make sure AI content doesn’t sound like everyone else’s?

The other thing CMOs aren’t asking enough: how do I get ahead, not just catch up? I hear too many CMOs asking how to get up to speed, how to catch up. That’s the wrong question. Two years ago the same CMOs were talking about how they were getting ahead. Now there’s a perception — mostly driven by LinkedIn hype and demo videos — that everyone else is further along. And perception becomes reality.

You’re a fractional CRO across roughly 15 startups a year. When does fractional actually beat a full-time hire — and when have you talked a founder out of bringing you on?

Fractional beats full-time when you need to build the foundation or reset, not run it forever. Pricing strategy, ICP definition, sales process design, GTM infrastructure — that’s a 90-day sprint, not a full-time role. It also wins when you don’t yet have enough commercial complexity to justify full-time: under about £2M ARR, with a sales process still being figured out, a full-time CRO is overkill. And when you need senior strategic horsepower without the overhead — a full-time CRO costs £150K+ in salary, plus equity and benefits, whereas a fractional engagement delivers the same strategic output in a focused sprint for a fraction of that.

I talk founders out of hiring me fairly often, and I think that’s a good sign. Sometimes founders assume that doubling my time gets output twice as fast. But there’s a point of diminishing returns. When you increase a fractional person’s capacity, they start joining more internal meetings and get pulled into keeping-the-lights-on work instead of high-leverage work. I’ve had clients move me from 10 to 20 hours a week, and honestly, the output didn’t double. I just spent more time in meetings that didn’t make me more productive.

When a founder just needs execution, not strategy, I used to refer them to a marketing operator or an agency. Now I’m finding AI systems can sometimes fill that gap alongside me — second-brain tools, workflow automation.

You work across Dubai, Europe, and New York, advising on crypto, AI tools, and B2B SaaS. What’s a commercial shift you’re seeing that hasn’t hit the LinkedIn discourse yet — and what’s the implication for senior marketers paying attention?

The shift: using AI to go deeper, not just to produce more. There’s a lot of talk about using AI to produce more content, do more outreach, and run more campaigns. Not enough about using AI to think more deeply about why you’re producing it in the first place.

The best companies are using AI inwardly, not just outwardly. They use it to understand their own story better — why does this product exist? What problem are we actually solving? To surface insights from customer conversations they’ve already had. To go deeper on positioning and messaging, not just write more copy, but understand the why behind it. To think critically about commercial strategy.

The implication for senior marketers: stop using AI as a production tool. Start using it as a thinking tool. Instead of “how can I use AI to write 10 blog posts this week?” ask “how can I use AI to understand what our best customers actually value, so we can position more effectively?” Instead of “How can I automate more outreach?” ask “How can I analyze the last 100 sales calls and find the objections we’re not addressing?”

The companies that win with AI won’t be the ones producing the most content. They’ll be the ones using AI to think more clearly about what to produce and why it matters.

Olly Jones is a fractional CRO and the founder of GTG Studio, where he builds revenue engines for B2B startups across fintech, SaaS, crypto, and AI. He spent 10+ years at American Express managing $3B+ global accounts, founded and exited the tech services company Kunai, and advises 15+ startups a year across Dubai, Europe, and New York. As Senior Operator and Partner at Myosin — a certified Claude Partner — he leads the Forward Deployed Marketer division, embedding AI-native marketing specialists inside B2B companies on 90-day engagements.

This wasn’t a paid post. We interviewed Olly because he had something worth saying. We’re always looking for collaborations, case studies, and sharp marketers with insights to share. If that’s you — a leader with a good story — just hit reply. We’ll sort out the rest together.

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It’s a personal story — my (Peter) personal project. Which means no NDA gymnastics, and I know every detail by heart.

Quick context first. Over the last year, AI has absorbed most of the quantifiable work in marketing. Generic copywriting, the wood-chopping level of design work, the basics of UX, almost the entire performance suite with advertising, analytics, and reporting, competitor analysis, anything that involves reports, summaries, and briefs, and the basics of strategy creation. Faster, cheaper, often better.

You might argue that your team, your super-unique specialism, is the exception. More likely, you just haven’t switched yet and are working manually out of necessity. Either way: AI is taking over your junior layer and much of your mid-management work, at a fraction of the time and cost. If a skill is quantifiable, AI is better at it — or the next model will be. That’s a race humans can’t win, so there’s no point entering it.

We have argued for a while that this final frontier will be taste. Human taste — the ability to select what’s unique, what’s worth keeping. Not the artist, but the gallery owner. Let AI create, let humans curate.

That thesis was incomplete.

First, I don’t want to hurt anyone’s feelings, but taste is not really needed sometimes, and most marketers don’t have any anyway. Marketing cares more about operations than creativity. Creativity, itself, is kinda itchy and funky. We don’t know much about it, but one thing sure we do know that it doesn’t fit on quarterly reports. It is not quantifiable. And because our clients, our bosses, and even us are obsessed with tracking, numbers, lists, and graphs, we simply trained out to care about creative things. Add the brainrot of social media to this and what kind of content we consume day-by-day, and we end up with a generation without any taste whatsoever.

So… why bother with taste?

But more importantly, what we underestimated is how fast the models would absorb the execution side of taste. Ask Claude today to build a website using 3-4 colors you provide, a short outline of what you like, and a few sentences about your brand. It will one-shot a site in minutes that most designers and developers would take two weeks to deliver — and I’ve personally watched designers sit on projects for weeks and produce worse. Taste as selection is being commoditized. Since taste is not really needed, we don’t really have it, and generic baseline of it works and does the job 95% of the time, and AI can do that automated, then, again… why bother?

But what’s left then?

What survives sits upstream of selection: the idea. The point of view that generates the selection criteria in the first place.

The idea is personal. It comes from your worldview and it points at the future: “I think this should exist, and I want to make it happen.” People call this agency, but agency without an idea is a hamster on a wheel — maxed-out willingness, nothing to aim it at, which describes most startup hustlers. The creation has to come from you.

Here’s how that works in practice.

The idea

I’m at the stage in my life where I can do whatever the f—ck I want. And for many years now, I wanted to completely stop working for others. And there is not enough money on the entire planet that is enough to pay me to sit in and manage a team, again. I loved doing this content business since it can be done from anywhere, anytime, and it brings enough cash that is reasonable.

So first, we toyed around the idea with Torsten to expand this AI-Ready brand. Start some sister brands around sales, customer experience, and creatives. Kinda holding the entire customer buying cycle. Certainly doable, but since I gave up client work a year ago, I realized something. I simply didn’t care at all. Not to mention that we are experts on marketing, but not experts on CX or sales. And since, I don’t care, I don’t want to be.

So I realized, what I want really is a passion project, a personal one. Where I combine my expertise (marketing + AI), my agency (I work like a monster if I care), and my passion together. The money? Yeah, it will take care of it itself, I’ll play the longgame. Again, I’m at the stage in my life where I can do this (takes years of saving and buying boring ETFs! :D).

From there, it was simple. I live in Italy, a country I chose. I wasn’t born here. I’ve been a foreigner here since 2021, but I know more and more about this place and its people, and I have a very solid point of view on how to enjoy this country as a foreigner or traveler.

So, was born. It’s a publication about the hidden Italy, 1,000 specific towns to be precise, offbeat experiences, local culture, and regional food. Written by a local expat (myself) for slow travelers and people who are interested in moving here.

From day 1, I committed to one rule: I don’t write about anything I haven’t personally experienced. The towns and restaurants must be visited, the experiences experienced, the food cooked or tasted by me, and me only.

Anywhere Italy

The 1,000 Italian communes most travelers skip. Written from Pietrasanta, Tuscany.

By Peter Benei

AI’s role

Let’s start with AI, because that’s what we want anyway, don’t we? From day 1, I built an AI project around this idea. The AI has four roles.

• The researcher.

  • There’s a very specific way the AI does research for this project. First, it ignores English-language sources, because most of them are shallow garbage about this country — yes, even the big names. Only Italian sources play. Maybe French or German.

  • Second, everything I produce should be grounded in research, because I can experience anything, but I can’t know everything. What I do with the research is up to me. I decide whether The Story about the not-so-famous Medici prince makes it into my coverage of a Tuscan hilltop town. I studied history and sociology, so I can take the findings and make them shine with my own point of view.

  • Bottom line: I don’t do research. AI does, based on my pre-defined needs. It saves me an enormous amount of time.

• The advisor.

  • AI acts as my business advisor. It handled the brand strategy, the initial research, the entire business setup, the marketing strategy, and the full content architecture. All of it interactive — I brought well-defined goals and needs — but how would I know how travel media make money and promote their content? Let the bot know that and advise me.

  • It’s also an ongoing consultant. A business strategy is not set-and-forget. It’s an evolution. Whenever I have questions, AI consults the existing data and helps me decide.

  • For this role, the AI has access to everything. And I mean everything: the brand book, the written strategy, the tone of voice, the full content archive, all performance numbers, all design elements. The more information it has, the better the advice.

• The builder.

  • The AI builds every digital asset. This project launched 2 months ago — during which I was fully offline for 2 weeks for personal reasons — and it already has a site, an app, and a database of 1,000 towns. Anywhere Italy Towns is the database is built on key filters: tourism factors, location, population, publicly available content, and more. I hand-selected the 1,000 towns from Italy's roughly 8,000 communes. AI collected, filtered, built, and made the whole thing searchable and presentable in 3 workdays. The database is interactive, either via the site or an app, letting you plan your slow travel based on your preferences and needs. It's free to use, and its main job is SEO and programmatic SEO for long-tail discoverability. Every plan generated is a page for SEO, forever, and it’s generated by users, not by me.

  • Both the site and content production run through an automated agent I named Marco. Marco knows every asset and the entire brand, lives on a VPS with access to everything, and runs around the clock. He maintains and refreshes the site, generates SEO content on it, connects the human-written content to the site, scans the competitive landscape, and reports on all of it.

  • Some workflows are fully autonomous — Marco runs them without me asking. Others are semi-autonomous: I trigger them with a single request, and the flow runs to completion on its own.

• The distributor.

  • Marco runs almost all social channels for this brand. X, Bluesky, Threads, Pinterest, and Facebook are fully autonomous, driven by a social media handbook and using human-written content. I don’t touch them. I don’t care.

  • Instagram is a prime channel and requires human handling, so Marco stays out. YouTube is fully human too.

  • Marco also sends me a weekly report on what my competitors have written. I turn that into a Reading Table post, quoting and linking their pieces. It doubles as my RSS reader, so I actually read them all, which is fun — they write about Italy just like I do. That post is a key growth lever, because it’s effectively a collaboration with multiple authors.

  • And Marco is my PR agency. He pitches podcasts, PR articles, and bloggers outside of Substack for collaborations. He has email capabilities and can be triggered by my email. We built this capability first for our AI Ready CMO agent, Jenny — she does the same, plus sponsorship outreach. It worked, so switching the brand over was a no-brainer.

The entire cost of AI usage:

• ~$100/month on the Claude Max plan + tokens

• $10/month on PostForMe, the scheduling app Marco uses for social

• $20/month on Hetzner, the VPS where all sites, assets, and Marco live

A rounding error on any marketing department’s budget.

Now, let’s see where I am in the picture.

Anywhere Italy

The 1,000 Italian communes most travelers skip. Written from Pietrasanta, Tuscany.

By Peter Benei

The human’s role

The “product” is not the planner or the towns page. The product is the content and the list itself. Monetization happens on both ends: you pay to read some content, or you buy items from the list. The latter isn’t activated yet — we’re in the growth phase — but it will be once the list is large enough.

None of it works if the content isn’t good enough. And how do you write about Italy, one of the most-covered topics on earth, without producing another 10-things-to-do-in-Florence piece? Only by writing from your own point of view. I live here. I experience the country the way only I can, and I have very strong opinions on some things and zero interest in others — which is a strong opinion in itself. AI has no opinion and no experience. It can fake a point of view if you hand it one, but it reads as generated within seconds.

So the human has four roles too.

• The writer.

  • Let me be precise, because it matters. There is plenty of AI-written content on the site. The towns pages and their copy? Almost all AI. It exists for distribution, it carries no personal touch, and I can’t scale it without AI. That’s fine.

  • Content published under my name is 95% written by me. The other 5% is the manuals — practical pieces that exist to help people do X. There, AI research is the core, AI writes most of it, and I’m essentially the editor adding my point of view.

  • But the Features about towns and experiences stay 100% human. Otherwise, what’s the point?

• The prime social media.

  • Instagram is our core growth channel. It’s Italy — people love the visuals. Our brand is slow travel, so instead of quick-cut, dopamine-fueled “44 things you must do in Italy this summer,” we make slow videos that calm you down. All done by hand — mostly by my wife, who runs that channel.

  • YouTube is second, also 100% human, same idea: slow, ASMR-like content about Italy. Usually me reading a Feature article out loud, plus a 10-20 minute video I recorded — a piazza, camera standing in one place the whole time.

  • LinkedIn is template-driven. AI helped with research and the templates, but due to platform limitations, I post manually, mostly written by me. I have 25K+ followers on LinkedIn, by the way. I stopped sharing AI+marketing content a couple of days ago, not because I don’t want to, but because it is so crowded and insane that it just doesn’t make sense anymore. However, I started sharing content about Italy. Oddly, it works. People love seeing stuff that simply just doesn’t belong to the professional spaces like LinkedIn. We are all humans afterall.

• The networking.

  • All networking is me. I network with other writers and expats covering Italy. I write my own Substack Notes, and it’s me in the Substack chats and comments. It doesn’t feel like work, because this is my passion — I couldn’t do it for marketing anymore, sorry.

  • It’s also me in Facebook Groups and Reddit threads. Honestly, I’d hand this one to a bot if I could — it’s a distribution channel that currently can’t be fully automated, and the limitation is purely technical. If you’ve figured this out, tell me. I’d pay for it.

  • And obviously, when podcasts, videos, and live sessions happen, it will be me. That can’t be faked — for now — but I wouldn’t fake it for this brand anyway.

• Improvisation.

  • Remember: no strategy is fixed. It’s an evolution, and the evolution of ideas runs on improvisation. In boring business speech, we call it pivoting. AI can’t pivot. Humans can.

  • Too early to tell — this project is a couple of months old — but I’m sure I’ll pivot later. It happened with AI-Ready CMO. It’ll probably happen here too.

So, what’s the learning here?

This case study applies well beyond passion projects — it maps onto marketing campaigns just as cleanly. As of today, we run on 5 rules for where AI ends.

1. AI can’t have original ideas. It produces reactive ideas — advising, conversing, brainstorming. If you’re looking for a truly unique idea, you won’t prompt your way to it.

2. AI can’t have a point of view. It can fake one, like it fakes opinions, experiences, and feelings. Only individuals have points of view. If you want a unique perspective, search for it within yourself.

3. Putting AI at the core of your brand is risky. Brand damage is real and PR crises are real. Where personal connection, touch, or networking matters, AI shouldn’t have any space.

4. AI can analyze but can’t get inspired. Agents improve within the feedback loops you build for them, but they only learn inside their own space. A human still has to look at the reports and decide what they mean.

5. AI won’t know where the limits are. Ask it, and it will generate the entire brand, all the content, all the strategy — relentlessly and endlessly. You decide the limits. You put up the barriers.

If you're curious about the project itself, join the ride here:

Anywhere Italy

The 1,000 Italian communes most travelers skip. Written from Pietrasanta, Tuscany.

By Peter Benei

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Now they show up with the answers already.

The buyer opens ChatGPT on a Tuesday afternoon, asks what the best platform is for their problem, and gets a synthesized shortlist in 30 seconds — including pricing, differentiators, and why one vendor fits, and another doesn’t. By the time they reach a human, the evaluation is done. Either you were in that answer, or you weren’t.

Jenna Hannon has watched this from a few seats. She was the first marketing hire at Uber Eats, ran the fractional CMO route across several startups, and now runs Hatter, an AEO agency working with brands like Runway and Swimply. Her argument is simple: the research phase moved into the model, and the job now is to be the answer the model gives.

We sent her our questions. Her answers are below, and as always, lightly edited.

You were the first marketing hire at Uber Eats, did the fractional CMO route, and now you’ve built an AEO agency. What broke about the playbooks you grew up on the moment AI search showed up — and what surprised you about how fast that happened?

What changed is your prospect’s behavior. ChatGPT is their trusted expert for every question, and that’s a fundamentally new channel with fundamentally new behavior.

The playbooks most of us came up on, whether that’s at a forward-thinking company like Uber Eats or in an incumbent, didn’t account for this.

New behavior requires new playbooks and new systems. That’s what marketers are going to have to build, and honestly, that’s what gets me excited. It’s why we started Hatter.

You’ve described a “Magical Moment” — a lead shows up to a sales call already sold because they did their research inside ChatGPT, not Google. Walk us through what’s actually happening in that buyer’s research, and what marketing teams need to start doing to get more of it.

The Magical Moment is when a lead shows up to a first sales call already sold. They know your pricing, your differentiators, and why you are the solution for them. The pitch is already done.

This is what changed with AI. Researching solutions used to be hard, even with Google. In B2B, a department head evaluating vendors would loop in procurement, wait weeks, sit through RFPs, and committee reviews. Now that same person opens ChatGPT on a Tuesday afternoon, asks “what’s the best platform for X,” and has a synthesized answer in 30 seconds.

It’s the same for consumers. People are going deeper before they buy because AI does the work for them. They’re having real conversations, asking follow-up questions, and making comparisons. And they’re trusting those recommendations.

So what do marketers do? Be the answer AI gives. Make sure your content addresses the real questions buyers ask at every stage of the journey. If that information isn’t publicly available, AI can’t surface it to your prospects during that key research phase.

You’ve publicly pushed back on the idea that AEO, GEO, and AI search are different disciplines. Why does the industry keep splitting them, and what does that distinction cost CMOs who buy into it?

Because new acronyms sell. AEO, GEO, LLMO, and AI SEO are all things agencies can define differently and charge for separately. But it’s wrong.

Whether you call it AEO or GEO, the work is identical. What matters is whether you’re showing up in AI answers and whether you have a strategy to get there. What’s important is that you have an AI-search-specific strategy that layers on top of your SEO efforts.

You’ve said the sources AI actually pulls from are probably not where marketers think they are. Where are they really — and what’s a realistic playbook for a B2B brand to build presence there without turning content production into a sweatshop?

Most marketers think AI is pulling from Reddit, LinkedIn, and YouTube. That’s not what we see in the data. What actually shows up in AI mentions and citations is mostly first-party websites and very industry-specific third-party content.

Think of your website as the foundation. Its job is to train models on who you are, what you do, why you matter, and when you should be cited. Most websites are maybe 5% of the way there. It’s like a knowledge base that only answered 5 questions and left 95 unanswered.

From there, the heavy lifting is third-party references in your specific industry: niche press, reviews, analyst coverage, the publications your buyers actually read. It’s rarely social media at scale.

The realistic playbook: start with the queries you want to show up for. Answer them one by one with owned content first. Only after that foundation is solid does it make sense to invest in earned coverage. Track your visibility throughout so you know what’s working.

That’s how you avoid the sweatshop trap. It’s not about producing more. It’s about making sure the right questions have clear, public answers before you go spend money amplifying them.

Hatter measures visibility, citations, and sentiment rather than rankings. For a CMO who has to justify the AEO budget to a CFO who only understands pipeline, how do you connect those metrics to revenue without faking the math?

Honestly, you should be tracking leads and purchases. That’s the real number.

AI visibility, citations, and sentiment are earlier indicators. They tell you if what you’re doing is working directionally before it shows up in the pipeline.

The connection between the two is difficult but not impossible. One thing we do for clients is embed AEO content in lead forms and conversion links so we can track when that content drives action. That’s how you start building a story that a CFO can work with.

Marketing leaders are being asked to fund AEO without giving up anything. If you had to pull the budget from somewhere existing — paid, brand, traditional SEO, events, content — where would you take it from first, and what’s your reasoning?

If you’re spending on technical SEO right now, stop and shift that budget to AEO content. The returns on technical SEO have diminished significantly, and that money is better spent on website content that actually answers real questions for prospects.

AEO should be your new SEO and website content strategy.

The brief is built on the same fundamentals: what your customers are asking, what your competitors are ranking for, and classic keyword research. It’s not a departure from SEO, but it is an evolution. The output has changed from optimization to content. You should expect to pay more for a good agency, as the work has changed and the quality of the content matters.

You work with brands like Runway and Swimply — companies that take this seriously. What’s the most expensive mistake you see smart marketing teams make when they decide to “get serious about AEO,” and what’s the underlying assumption that produces it?

Most teams hand AEO to their SEO agency because it feels close enough. But an SEO agency that just pivoted to sell AEO usually doesn’t have the content team to execute it well. Yes, you draw from SEO strategy to inform what you’re doing, but the output is quality content that teaches LLMs about your company, your products, and your services. That’s classic content marketing, not SEO optimization.

The underlying assumption is that AEO is a technical problem. It’s not. It’s a marketing problem. Look for Product Marketers and Content Marketers to lead this, whether that’s inside your team or at the agency you hire. Those are the people who know how to explain what you do in a way that actually resonates, and that’s exactly what AI needs to cite you with confidence.

Jenna Hannon is the founder and CEO of Hatter, an AEO agency helping brands show up in AI-generated answers. She was the first marketing hire at Uber Eats and has served as a fractional CMO at MultiSensor AI, FORM Kitchens, and Kyte Care Rentals. Hatter works with brands including Runway and Swimply.

This wasn’t a paid post. We interviewed Jenna because she had something worth saying. We’re always looking for collaborations, case studies, and sharp marketers with insights to share. If that’s you — a leader with a good story — just hit reply. We’ll sort out the rest together.

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See, if there is chaos around you, and let’s be honest, most of the work we do as marketers is quite chaotic, your natural instinct is to seek control. If you can’t control the big things, obviously, so what are you going to do? You control the little things.

I am not even joking, how many times have I been in meetings about single Facebook ad copies? Or the CEO of the agency started typo-correcting an email campaign’s copy live, alongside the creative team. Most people would say, yeah, he pulled up his sleeves, wasn’t afraid of the work! Yeah, I would say it’s lunacy, micromanagement level 10,000, the most unproductive thing you can do as a leader, the absolute worst way to use your time at a company that you are leading. Celebrating this type of behavior is madness.

Thanks to AI, you don’t have to do this anymore.

The thousand reversible, informed, time-consuming decisions underneath a big-level strategy, the small minor details that are either driven by data, or niche specialist knowledge, or both, have all been automated in the last few months.

The strategy itself? No, that’s still you. That’s worth your time. That’s worth your meeting with others. That’s worth your team.

But if in the second half of 2026, you’ll either get pulled or set up a new meeting about grandiose things like a Facebook ad copy discussion, my honest advice is a simple word: run.

Let AI handle this. Focus on the things that matter. This week, entire campaign functions got automated, see below.

Seedance 2.5 might have just moved AI video into a new league

A funny thing has happened in AI video over the past year. Every new model claims to be “cinematic,” every demo gets called “Hollywood-quality,” and every launch is apparently the one that changes everything.

Most of them don’t. ByteDance’s new Seedance 2.5, however, might actually deserve some of the hype:

Early reviewers have been unusually enthusiastic, describing it as “game-changing” and “unbelievably powerful.” More importantly, they’re all pointing to the same thing: consistency. The model promises 30-second videos generated in a single pass, far more stable characters and products across longer scenes, and support for up to 50 (!) reference images. If those claims survive independent testing, they’re much more significant than another incremental jump in visual fidelity.

Length isn’t really the story here.

The real bottleneck for commercial AI video has never been image quality. We’ve already crossed the threshold where a six-second AI clip can fool almost anyone. The problem has been keeping the same product, the same character, the same lighting and the same camera logic alive for more than a few seconds without everything quietly melting into nonsense.

Marketers should care much more about consistency than duration. A 30-second clip is nice. A 30-second product commercial where your bottle doesn’t mysteriously change shape halfway through is considerably nicer.

There are still plenty of unanswered questions. Seedance 2.5 is currently in enterprise beta, pricing hasn’t been announced, and many of the most exciting features still come from ByteDance’s own presentations rather than independent benchmarks.

Still, the direction feels increasingly obvious. AI video is slowly escaping the “look what I made!” phase and entering the “let’s actually ship this campaign” phase. Some companies will still spend the next year generating astronauts eating spaghetti.

There’s one problem with OpenAI’s ad strategy

Funny story: every company that reaches enough scale eventually rediscovers advertising.

Google did. Meta did. Amazon did. Netflix resisted for years before eventually joining the party. Now OpenAI is now making it very clear that ChatGPT ads aren’t a side experiment.

At Cannes, OpenAI shared a few encouraging early numbers. Apparently, users are dismissing ads about half as often as they did when the pilot launched earlier this year, and roughly 20% of ChatGPT conversations already have commercial intent. They’ve also lowered entry costs, expanded partnerships with companies like Criteo and StackAdapt, and continue building the infrastructure advertisers expect.

So far, so good.

Then comes the forecast.

OpenAI reportedly believes advertising could generate more than $100 billion annually by 2030. That’s where things become considerably harder to believe.

WPP recently projected the entire AI search and chatbot advertising market to be worth roughly that amount by the end of the decade. Not OpenAI’s slice. The whole market—including Google, which, last time we checked, isn’t planning to quietly leave.

Forecasting a market that barely exists is admittedly difficult. Nobody knows how people will behave once conversational interfaces become the default way of discovering products. Maybe AI ads become the next search ads. Maybe they don’t.

Whether OpenAI reaches $100 billion is almost beside the point. The interesting question is whether conversational advertising becomes the next major customer acquisition channel. And that answer probably won’t come from a keynote in Cannes.

It’ll come from your performance dashboard.

Meta wants to become your marketing department

It’s easy to dismiss Meta’s Cannes announcements as another pile of… incremental AI features. Even if the list is long (brand Memory, better copy generation, creator updates, messaging agents, creative strategy space, business agents, creative approvals, translation, voiceovers, phew that was long), individually, none of them feel particularly earth-shattering.

Together, though, they’re telling a much bigger story.

Meta is trying to eliminate the gaps between every stage of digital marketing. You start with a product catalog and a campaign brief. AI generates the creative. It adapts it to your brand guidelines. It identifies suitable creators. It launches campaigns. It optimizes delivery. It follows up with customers through Messenger or WhatsApp.

Increasingly, the entire customer journey stays inside Meta’s ecosystem. That’s a very different ambition from simply making better ads.

We’ve spent years thinking about marketing as a collection of disconnected tools. One platform for creatives, another for influencer management, another for media buying, another for customer support. Yet another for reporting.

Meta is betting that the winning product isn’t the best AI image generator or the smartest chatbot. It’s the workflow.

That’s also why we wouldn’t obsess too much over Meta’s headline ROI figures. Yes, they’re reporting stronger returns from AI-powered campaigns, and some of those numbers are certainly impressive. But internal benchmarks always deserve a healthy dose of skepticism.

The more interesting competitive advantage is structural.

Every additional AI feature becomes more valuable because it feeds the next one. Every creator interaction strengthens campaign creation. Every campaign strengthens recommendations. Every customer conversation improves future targeting. Every new business that joins the platform makes the whole system slightly harder to leave.

Google is building something similar. Shopify is trying too, as you will see in just a second. Increasingly, the AI race isn’t about building the smartest model, which Meta definitely does not have. It’s about owning the entire workflow before someone else does.

Shopify’s Campaign Autopilot is exactly what it sounds like

Remember when building an online store meant… building an online store?

Somewhere along the way, e-commerce owners accidentally became media buyers, lifecycle marketers, email specialists, copywriters and attribution analysts. Shopify clearly thinks that’s ridiculous.

Its new Campaign Autopilot is essentially an in-house marketing agency that lives inside your Shopify admin. You set a budget, define a few guardrails, connect your channels, and the AI plans campaigns, allocates spend across Meta, Shop and email, launches them, and keeps optimizing over time. More channels—including ChatGPT Ads, Microsoft Advertising and Snapchat—are already on the roadmap.

Notice what’s missing.

Shopify isn’t trying to become another AI image generator. It isn’t promising Oscar-worthy copywriting or miraculous creative ideas.

The emphasis is on orchestration. Taking dozens of small marketing decisions that happen every day and quietly making them on your behalf.

Of course, there are limits. Campaign Autopilot still relies on your existing product catalogue and assets rather than inventing entirely new creative, and Shopify is refreshingly honest that results aren’t guaranteed. The system needs time to learn your business, and you still decide how much autonomy you’re comfortable giving it.

But the trajectory feels familiar.

First, AI helped you write.

Then it helped you design.

Now it’s starting to decide.

Adobe stops chasing AI benchmarks

Adobe has spent the last two years fighting an increasingly difficult battle.

Not because Firefly is bad—it isn’t—but because every month seems to bring another AI image model that’s faster, cheaper or simply more exciting. Competing feature-for-feature against startups is a game very few incumbents win.

Adobe seems to have accepted that.

Instead of trying to build one magical AI that does everything better than everyone else, it’s making AI disappear into the software people already use. Firefly AI Assistant is now available across Premiere, Photoshop, Illustrator, InDesign and Frame.io, while the new Firefly Studio keeps projects, assets, characters and creative context together from the first idea all the way to production.

That’s a much smarter place to compete.

Most professional designers don’t actually spend their day generating pretty pictures. They spend it searching for assets, adapting formats, maintaining brand consistency, reviewing feedback, preparing variants and trying not to lose their minds after opening their seventeenth application before lunch.

Adobe is automating that work.

And that’s probably the bigger opportunity across the entire AI industry.

The models themselves are becoming increasingly interchangeable. Today’s breakthrough quickly becomes tomorrow’s commodity. The harder problem—and the one customers are much happier to pay for—is making all those models fit naturally into the workflows they already have.

Some companies will keep competing on benchmark scores. Adobe seems increasingly interested in competing on something much harder to copy: not making AI better, but making creative work feel a little less like administrative work.

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The human isn’t always in the room anymore.

Your buyer opens ChatGPT, types “best [category] for [problem],” and reads back a shortlist before anyone at your company knows they exist. The model doesn’t repeat your sales deck. It repeats whatever shows up most consistently about you and your category — written by anyone, everywhere. Which means brand language stopped being decoration. It became the thing that decides whether you get found or cut.

Lauryn Warnick has spent two decades on that problem — first inside the brand industry’s top tier, Wolff Olins, Lippincott, and Landor, then in the operator’s seat running marketing at Chartbeat, now as founder of Villain Branding.

She writes and hosts The Brand Equation, a newsletter and podcast where senior brand leaders talk about what brand actually does for the business. Her working line: average is the most expensive thing you can buy.

We sent her our questions. Her answers are below, lightly edited.

You’ve repositioned enterprise B2B companies through their highest-stakes moments for two decades. What broke in the last 18 months that wasn’t broken before — and how is this failure mode different from the cyclical repositioning you’ve always done?

We used to talk about “the room.” Get your story sharp enough that your best rep or exec can walk into a pitch and carry it home. The rep was the delivery mechanism. The human. But we all know the human in the room isn’t always the thing anymore. We’re building for the (ever-changing, totally black box, hard to understand-let-alone-influence) model.

Your buyer opens ChatGPT or Perplexity, types “best [your category] for [their problem],” and reads back a shortlist before a single human at your company knows they exist. And the model doesn’t repeat what your rep is saying aloud. It repeats whatever language shows up most and most consistently about your brand and your category.

Bain and Google have research on the “day-one list” (the vendors a buyer already has in mind before they do any real digging). 86% of buyers have that kind of list, and 92% end up buying off it. That list used to form from reputation and word of mouth, but not anymore. It forms from whatever the bots read about you.

So I’m seeing the old failure mode as getting beat in the room. Now you’re getting cut before the room even exists. It’s rough out there...

Your method positions a brand against an enemy — a competitor, an incumbent, a lazy assumption. In the AI era, what’s the most common wrong villain you see B2B companies pick, and why does it leave them looking identical to everyone who picked the same one?

So many people are picking the same villain right now. They’re all against what they define as “legacy.”

Every B2B company with an AI story decides the enemy is the old, slow, manual way of doing things. That isn’t great. You don’t want to be that. I agree with that premise. But until you realize the forty companies you compete with all picked the identical enemy, you’re not going to stop sounding like the same press release.

Every single SaaS org is writing something like “ditch the legacy approach” or “move faster with [insert their AI thing they think is novel].” That makes you no longer a unique brand. It makes you all the same category.

What we try to get our clients to see is that a real villain is specific enough that your competitor literally couldn’t put it on their own homepage. Salesforce went after software itself. Remember that little red NO SOFTWARE circle they used? Their competitors couldn’t steal that, because their competitors were software. Liquid Death went after boring water and plastic bottles. Find those edges and you stand out to LLMs as much as to the people you’re selling to.

A lot of times CEOs of tech companies will say “but we do it better” and so they want to claim the same positioning. That may be true, but you’re NEVER going to get to the point of proving what “better” means to a customer if they can’t find you. And in the AEO world we’re in, they won’t find you if you sound just like them... better or not.

You say mid-size B2B teams are stuck between “product speak” and “buzzword soup.” What does each actually sound like in a sales deck — and what does the working third option look like?

I could talk about this for hours (maybe that’s a sad thing to admit!).

Product speak is like if your feature list or Jira tasks or changelog got thrown on your homepage. You see things like “Configurable workflows, role-based permissions, real-time sync.” All true! I’m sure all great! But they tell me nothing about what changes for the human reading it. It’s a parts list cosplaying as messaging (or worse, positioning). This is happening A TON with AI right now. I’ll die on the hill that AI is a capability. It’s not your positioning. It’s not even a category. It’s a way of working. Like “Digital” was back in 2007.

Buzzword soup is the overcorrection on trying to sound clever and disguise those product points. “The intelligent platform for modern teams.” So vague it floats way too high for anyone to actually use it. Can you throw your competitor’s logo above the headline and have it still be true? That’s where buzzword soup comes in.

So product speak = too low to matter. Buzzword soup = too high to differentiate (or land at all).

“Your team stops rebuilding the same report every Monday morning” beats, well, pretty much any sentence with the word “orchestrate” in it.

Stripe nailed this. Their whole pitch was “a few lines of code and you’re taking payments today,” aimed dead at every developer who’d survived a six-month, sales-gated, call-us-for-a-quote integration nightmare. Drop it in over coffee, you’re live. A developer reads that and knows exactly the relief that’s coming.

Usability is hugely important, too. It needs to work on your site, but your sales rep also needs to say it out loud without cringing and cursing their marketing team. Most importantly, your buyer can repeat it to their boss without a translator. Some people use the “cocktail party” or “elevator” story. I think it’s more “can a tired VP retell it in a hallway without cue cards?”

Your promise is a story that travels across teams and decisions without breaking. Where does a fragile story break first in an enterprise B2B company, and what does the symptom look like for a CMO who doesn’t yet know that’s the root cause?

Sales enablement. Every time.

It’s actually a big reason why we’re often brought in. What happens is the story makes it through launch just fine. Leadership signed off, the website’s gorgeous, everybody clapped at the all-hands. Lots of kumbaya moments. Then it gets properly stress tested by the people who have to say it out loud forty times a week. Once it’s not making their lives easier because it’s “too clever” or “too complicated” or “not specific enough” or whatever the reason is, they ignore it. They build their own decks. They re-explain the company from scratch on every call. Then the sales cycle gets longer. Cross-sells and upsells get harder (because the story’s more complicated than it needs to be).

The biggest challenge is that no one actually agrees on the problem.

The CMO sees “we have great messaging but reps aren’t using it” and thinks it’s a training problem. In some cases, it very well could be. They just need to make it more usable. But MORE isn’t usually the thing they need. It’s less, but sharper. So more enablement. More decks. More Gong reviews. None of that solves the problem. It’s that the story was built for consensus and approval. Nobody built it to survive actual deal conversations. Those are two completely different jobs. One rewards nuance and caveats, the other rewards a sentence you can say drunk (or tired!) at a conference.

Avis figured that out in 1962: “We’re number two. We try harder.” Hertz couldn’t touch it. That’s a story built to be repeated.

Your sellers are closing deals in spite of your brand. They’ve each duct-taped together their own pitch, and they’ll keep using it until the official story is easier to say than the one they invented.

A CMO making the case for repositioning has to sell it to a CEO and CFO. What’s the most defensible way to put a dollar figure on misalignment?

I’ve found you have to put a number on the bleeding the mess is already causing, pulled from the data the CFO already trusts.

Three numbers that move in the wrong direction when there isn’t brand consistency.

1. Sales cycle length stretches because every deal starts from scratch — the buyer re-learns who you are at every stage.

2. Win rate against your number-one competitor drops because the second you leave the Zoom, the buying committee has to retell your story to each other, and if they can’t, they default to the competitor whose story they can repeat.

3. Ramp time for a new hire drags because there’s no clean story to hand them, so month one is them inventing their own or unlearning and relearning four different stories. This is actually a big reason why CMOs call us. They come into the organization and after a month they say “I’m STILL trying to figure out what we do and why. We need to get to clarity quickly.”

Now the ask changes shape. You walk into the CFO’s office holding a leak in the funnel they already watch every Monday. The brand refresh is a cost, yes — but they’ll fund it once they see it’s being paid back by the shorter sales cycle.

That’s the key to shifting mindsets from brand-as-expense to brand-as-revenue-driver. Simply: don’t lead with brand.

You put CMOs and CEOs in the same room. Where do they most often disagree about brand work during an AI-era repositioning — and what’s the disagreement actually about underneath?

The CMO wants to move the story forward, the CEO wants to protect what’s already there. Especially if that CEO is the founder, of course, or even if they came up through engineering or product.

What it takes them a little bit to see is that they’re actually both scared of the same thing: losing something. The CEO has watched the current positioning build pipeline for years and hears “reposition” as “set fire to the thing that’s working.” You and I know that the first rule of branding is “do no harm,” but the CEO doesn’t know that. They think they’re losing everything they’ve worked for. It’s scary!

The CMO is closer to the market and can feel the story going stale, getting vanilla, blending into the background. What she’s losing is distinctiveness, the thing that made buyers pick you on sight. She can see the day it’s gone for good getting closer and closer. That’s scary too!

They’re both just doing their job. It’s about whether you can change the words without torching the equity you’ve banked in the old ones. And the answer, of course, is... of course you can. You should! The equity and the exact language aren’t welded together. You shift perception by taking short cognitive steps forward for the buyer.

So most of my job in that room is boring. I make them say out loud what they’re keeping before anyone’s allowed to argue about what changes. And we always start with what we recommend they keep and build on before talking about what may need to change. Most of the fear evaporates right there.

You frame brand as a revenue tool, not an aesthetic exercise. At enterprise scale, how do you actually prove the positioning work moved the business and not just the marketing metrics?

I won’t talk to you about attribution. And we of course do full brand tracking studies prior to and after a launch. I’ll also say, we (and a lot of people smarter than me) are constantly working on a better way to measure brand value without adding more dashboard theatre to our clients’ fatigue.

The simplest way is that we baseline four things up front: sales cycle, win rate against the named competitor, average deal size, and what percent of deals used the actual company story versus a rogue version. Then I watch those again after we launch with the new story.

Impressions and share of voice don’t prove a thing here. Instead we look at the usual aided and unaided awareness, consideration, etc., but also at how many people stop building custom decks, how deals move faster, how often you start winning the head-to-heads you used to lose.

Gartner says buyers spend only about 17% of the whole buying journey with sales (and that’s all vendors combined).

If the only needle that moved is brand awareness, cool, but I just ran a marketing project. The bar for us is whether selling the thing actually got easier.

You’ve said you have “a habit of talking people out of giving you work.” When does a CMO not actually need a repositioning, even when they think they do — and what’s the cheaper, less glamorous fix you point them toward instead?

More often than you’d think — and I’ll talk them out of it, which my CFO hates. This is one of those things that the founder of an indie agency can do that most of my former employers can’t.

What I usually see is that the brand book or the positioning language is actually really strong. It’s just sitting in a PDF nobody opens. Reposition that company and you’ve handed them a shiny new document to ignore. Now they’ve got two really smart, totally unusable things. It’s irresponsible, honestly.

The better fix is way less glamorous. Take the story they already have and make it impossible to avoid for marketing, sales, product, HR, CX, and execs. That means doing the tough but crucial operations and systems work. Things like building the sales deck so the official narrative is the path of least resistance and the rep actually uses it. Or baking it into onboarding so new hires learn it before they pick up bad habits. We’ll even script things like earnings calls or keynotes so the exact words everyone else is seeing are written into what the CEO says publicly.

Honestly, sometimes this is way harder to do. We have a few folks on our team who do nothing but the operations and enablement work (much different from execution marketing) and are held to metrics of usability and cross-team penetration. Again, it’s not always sexy, but it makes the measurable and monetizable difference.

And since I’m the founder of the company, I have a lot to lose. Frankly, I’d rather lose the revenue than sell someone a cure for a disease they don’t have and have the word get out that I did. There are too many agencies like that. I have no interest in it.

What’s the most important shift in B2B brand work happening right now that senior marketing leaders should be paying attention to — but that nobody in their LinkedIn feed is writing about yet?

It’s funny you frame it as “not writing on LinkedIn,” because I’ve absolutely written about this and it’s gotten such little engagement, but I stand by it!

What’s so important to know is that your brand is training data right now.

Every model your buyers ask for a recommendation is learning your category from whatever’s most repeated about it online. And right now? That language is being written by your competitors, the analysts, and some guy on Reddit. You’re letting other people teach the machine what your category means, then acting surprised when the machine doesn’t recommend you.

Still, in the second half of 2026, so many CMOs think about AI as the thing they bought to write blog posts faster or create cool semi-on-brand images for LinkedIn carousels. Meanwhile it’s now become where the shortlist gets built, before a human is anywhere near it. AI is a capability, not a category. Same goes for how you use it on brand: the real work is making sure the machine repeats you correctly.

Lauryn Warnick is the founder and CEO of Villain Branding, a verbal-first brand strategy agency that works with enterprise B2B tech companies through their highest-stakes moments — post-acquisition repositioning, category launches, and the runway before a raise. She writes and hosts The Brand Equation. Her working line: average is the most expensive thing you can buy.

This wasn’t a paid post. We interviewed Lauryn because she had something worth saying. We’re always looking for collaborations, case studies, and sharp marketers with insights to share. If that’s you — a leader with a good story — just hit reply. We’ll sort out the rest together.

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AI voice.

By now, we can agree that AI generates copy, visuals, video, audio, and design better than 80% of human versions. In some categories, better than 90%. In a few cases, using a human no longer makes sense. And it’s only getting better.

There are two places AI is not there yet.

One is long-form video. The bottleneck isn’t capability. It’s infrastructure. We don’t have a fully AI-generated Marvel movie because we’d need to dedicate a full data center to make one. Technically doable, financially insane. For now.

The other is long-form AI voice. Anything past 10 minutes, or anything that needs to carry actual emotion. For a five-minute product explainer or a cold business speech, AI voice is fine. The longer it talks, and the more emotion it has to carry, the more it sounds like a walkie-talkie pretending to be a person.

I know because I tried. I started a newsletter about Italian lifestyle (read it here), and one of the marketing angles is an audio version. The posts are closer to literature than travel writing — long, emotional, a 15-minute read. You slow down, you read. That’s the idea. Not a 10-things-to-do-in-Florence bullshit list that anyone can write. But a long feature on a hidden town. Some of these posts are read aloud by me. So, I did what anyone experienced with AI would do: signed up for ElevenLabs, top tier, cloned my voice using over an hour of recordings, and pasted it into a post.

It was hilariously bad. By that point I’d recorded one episode myself, so I had something to compare against. I have a weird accent — Eastern European, British English, harshest Texan — and I don’t care if I lose it. But the intonation, the rhythm, the emotion, the feel of the audio. It was a bot reading a script. Which is what happened, I know, it’s ElevenLabs, hello. I tried editing the script with intonation cues, and ended up spending more time tweaking than I would have spent just recording it with a microphone. I canceled.

And that defines our rules. Short, transactional, no emotion? Use the bot. It will cost less than a creative’s salary plus 100x less than a production company. But long-form, emotion-led content with a brand attached to it? Right now, it’s a reputation minefield. The longer the bot speaks, the more ways you can tell. Our voice is inherently human. How we read aloud is inherently imperfect: we make mistakes, mispronounce words, and miss cues in the script. It’s all part of it. It’s all why it is human. Losing these, you are either a super-professional voice actor (which you aren’t) or a bot.

Emotions are hard to fake. Even between humans.

Claude Design Comes for Figma and Canva

We called Claude Design incredible when it launched. Turns out incredible-but-unusable is a real product category, and that’s roughly where it sat. A PCWorld reviewer torched 80% of his weekly Pro allowance in 25 minutes making three versions of one webpage. Fun demo, terrible tool.

But this week’s update might change that. The new editor lets you drag, resize, and align elements directly on the canvas, so you’re not burning a full model turn every time you nudge a button two pixels left. You can export to Adobe, Canva, PowerPoint, and a pile of others. And it now shares usage limits with chat, Cowork, and Code instead of drinking from its own tiny pool.

The real story is the Claude Code round-trip integration. You can pull your design system in from your existing website code, build against your actual components, and hand the finished layout to Claude Code, which picks up where you (ok, your dev team) left off.

The handoff between design and engineering has been a resentment factory for twenty years, and the bet here is that if one system does both sides, there’s nothing to misinterpret.

Whether it holds up on a real enterprise component library is the open question. But this is the first version that looks like a daily tool instead of a party trick.

AI Search Ads Are About to Become the Fastest-Growing Channel in Advertising

ChatGPT has been running ads for less than six months. WPP Media now thinks the category it’s creating gets to $100 billion faster than any media channel in history.

Quick recap on the math, because the headline number doesn’t actually come from WPP. OpenAI floated $100 billion by 2030; WPP’s own model is more conservative, putting the crossing point about six years out if current momentum holds. Either way, the speed is the point.

Traditional search took 22 years to reach that scale, social about 14, retail media around 10. Generative search in six would be a record by a wide margin.

This year it’s a rounding error: $5.1 billion globally, the US taking roughly 60% of it.

Where does the money come from? Partly from your existing search budget, partly from commerce, partly from the universal marketer urge to plant a flag on new real estate before a competitor does. The thing nobody can buy yet is control. There’s currently no way to buy ad space specifically next to AI Overviews, or to exclude the format.

So the forecast is real, the spending intent is real, and the actual product you’d spend it on is still half-built. Watch this one, but don’t let anyone tell you the playbook exists yet. It doesn’t.

AI Now Sends Twice as Much Traffic to Your Store — and It Converts Better

If you’ve been treating AI referral traffic as a novelty line in your analytics, it is becoming serious. Traffic from AI sources to US retail sites grew 138% year-over-year in May, and it’s up over 1,300% since Adobe started tracking in October 2024.

This isn’t garbage firehose traffic. AI-referred shoppers converted 54% better than non-AI sources — a complete reversal from a year ago, when AI traffic converted at roughly half the rate. They spend 53% longer on site, browse 23% more pages, and bounce far less. An AI visit is now worth 53% more than a non-AI one. Twelve months ago, non-AI visits were worth 128% more.

So what do you do? The boring, unglamorous thing: make your content machine-readable.

Adobe found cosmetics and electronics sites lead on AI readability; grocery and furniture lag because their page design suppresses AI citation. If your specs live in a PDF or your product story is buried under an animated, undoubtedly amazing-looking illustration, you’re invisible to the systems sending the best traffic you’ll get this year.

Fix that and you’ve helped your humans too.

The Biggest Change to Ad Targeting Since Targeting Existed

Digiday published a vector-based targeting explainer this week, and if you do any media buying, you should read the whole thing. The short version: keyword and audience-segment targeting are about to look like checkers next to chess.

Here’s the idea, stripped down. A vector is a set of coordinates — like latitude and longitude, except instead of two dimensions it can have thousands. Those dimensions let you place a piece of content, or a person, in space and measure how close it sits to everything else. Instead of picking keywords, you’d set a target vector and a radius — how far you’re willing to expand from that point. Keyword blocklists famously kill ads on “mobile phone” articles because the list contains “mob.” Vectors fix that by clustering on meaning, not letters: “mob” lands near other organization types and near crime, depending on the dimensions.

This is lookalike targeting’s smarter cousin. As data updates and embeddings recalculate, the coordinates move, so you can track trajectories and forecast someone’s propensity to buy.

The catch worth knowing — the vectors only relate to each other if they’re built with the same embedding model. Which means whoever owns the model owns the targeting layer. LiveRamp’s already donated a protocol to IAB Tech Lab to standardize this.

It’s early, it’s abstract, and it’s going to reshape how you aim a campaign. If you feel lost, read the explainer. You’ll be a year ahead of most people.

OpenAI’s “GPT-Bidi” Could Make Voice AI Actually Work

This one is to watch, not to act on — yet. OpenAI looks to be prepping a major voice upgrade built on a new architecture leaked as GPT-Bidi — short for bidirectional. Reporting suggests it can listen and speak at the same time, absorb interruptions, and change course mid-sentence instead of freezing the second you say “mm-hm.” If you’ve ever talked to a voice assistant and felt like you were trading walkie-talkie transmissions, that’s the problem it’s built to kill.

This is from leaks and product-code sightings, not an announcement. The name could change before launch, and the timing isn’t clear. So treat the specifics as soft.

But the direction isn’t soft at all. OpenAI’s text models sprinted ahead while voice stayed on an older stack, and closing that gap is the whole bet behind their audio-first hardware ambitions.

The moment this becomes available for outside developers, fully voice-driven customer service agents stop being a demo. And that might be by year’s end.

If your support org runs on phone queues and IVR trees, or if your sales team is on the phone 24/7, this is the technology that finally comes for them.

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AI can make decisions at least as good as humans' but 100x faster on issues supported by data. So humans should only stay in the loop for the input (managing what data AI should look into and why), and for the review of the output (just to safeguard that it is the right thing AI produced). For the rest, it’s all automated.

We thought that taste, design, aesthetics, and grand-level strategic decisions would stay human. AI-powered, but still human-driven. Yes, there are some elements even there where AI will take over, like UX design is mostly data-based, but we thought AI won’t be able to produce amazing frontend, layouts, and full brand designs. Also, AI won’t be able to produce entire campaign strategies based on custome…

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Because buyer behavior changed, AI changed how teams work, and revenue expectations didn’t change at all.

We’ve recently sat in on a prep conversation with four B2B CMOs from Spacelift, Nexla, Oligo Security, and Hydrolix ahead of an upcoming roundtable on pipeline growth.

A few comments stuck with us:

• One CMO said prospects are increasingly finding them through ChatGPT and other LLMs, forcing their team to rethink how content is created and distributed.

• Another argued that AI is actually making brand more important because every company can now create content at scale, making differentiation harder.

• One described their product organization's shift from monthly to weekly releases, creating entirely new challenges for marketing, positioning, and enablement.

• All of them agreed on one thing: despite all the new tools, they’re still judged on pipeline, revenue, and closed-won business.

What we…

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This week, several of the biggest AI companies made it very clear that the loop is on its way out. Reporting, analysis, planning, even execution are being pulled into a conversational layer where the marketer talks and the system does. The dashboard turns into a chat window, the export into a question, the Friday performance review into a thread.

We said last week that AI would own the customer’s decision cycle. This week, it came for the marketer’s work cycle too. Both sides of the marketing function are being pulled into the same conversational layer simultaneously.

The reflex will be to wait. To ask IT. To commission an audit. To pilot it in Q3. That reflex is ex…

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And you would think it’s the boomers and Gen X. The generations sitting in the armchairs and corner offices are the ones who lie the most, right? By a planetary distance, it’s Gen Z.

Now, we are not just less nice, but also very pragmatic. And, to be honest, old. We’ve seen a lot. So we assume innovation cycles are called cycles because they repeat. The one we keep coming back to here is the late-90s digitalization cycle. You know, when everything moved from paper to the cloud. There was a thing called the Internet, and everyone wanted to have something online.

What happened there? Jimmy, the 60-year-old boss, who’d seen a computer at a tech expo once but still ran his business through his Rolodex and his secretary, and who was an avid fan of Dale Carnegie self-help books, saw a blue ocean opportuni…

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Social media made that jump fifteen years ago. It started as a way to share photos and ended up rewiring elections, identity, and the news. AI is making the same jump right now, and this week made it obvious.

The clearest signal was the Monet story recently.

Someone posted a real Monet painting on X, labeled it AI-generated, and thousands of people lined up to explain why the “synthetic” brushwork and “soulless” composition gave it away. It was a real Monet. One part of this story is about how culturally uneducated people are these days, so that they can’t name a very famous artwork. But that’s just half of the story.

Research now backs up what that moment showed: people downgrade the exact sam…

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Google added an “Agentic Browsing” category to Lighthouse, the audit tool most web teams already use. Android started letting Gemini build carts, book rides, and fill forms across apps from a power-button press. Stripe and American Express rolled out payment systems built specifically for AI agents, including an Amex liability framework for when agents make purchasing mistakes. And we finally got real numbers on how long it takes for a new page to appear as a citation in ChatGPT and Claude.

For marketers, this is the week the agentic web stopped being a conference panel and started being plumbing. Browser standards, mobile defaults, payment rails, citation timelines. The unglamorous parts of the stack that decide what actually becomes a requirement versus what stays a demo.

The practical question over the next few quarters is whether your site, product data, checkout flow, and …

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Katie’s core argument: the moat for a lean DTC founder isn’t a bigger team or a bigger raise. It’s a stack of narrow agents — social listening across Reddit, TikTok, reviews, and surveys; a chief of staff that briefs her every morning; a demand-planning system that frees her merchandiser from spreadsheets — all wired to her specific business questions. Off-the-shelf tools answer generic questions well. They don’t answer hers. The unlock was deciding to stop paying for fragmented subscriptions and start designing the system she actually needed.

About Katie Blouin

Katie Blouin is co-founder and CEO of Larken, a maternity and postpartum apparel company best known for its patented all-in-one nursing and hands-free pumping bra. She launched Larken seven years ago with her former law school roommate after both struggled with the same pain point during their second pregnancies. The company has been bootstrapped from day one, has appeared on the Inc. 5000 list multiple years running, manufactures entirely in the U.S., and operates with two co-founders, an outside marketing agency, and a network of contractors — most of whom are mothers themselves. Before Larken, Katie spent time at McDonald’s and across other large CPG environments. She is based in Atlanta.

You can find Katie on LinkedIn, at katieblouin.com, or follow Larken at shoplarken.com and on Instagram at @larken_shop.

About Peter Benei

co-founded the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn or read his newsletter.

Top 10 Takeaways

1. Bootstrapping forces discipline that VC-backed founders never develop. Every dollar and every hour has to earn its place. That constraint is uncomfortable in year one and a structural advantage by year seven. The founders who stay lean longest tend to build the cleanest decision-making muscles.

2. An agency-and-contractor model gives you specialist access that a single hire can’t. When your needs change every six months, a fixed in-house marketer becomes either underused or overwhelmed. An agency stack lets you pull different levers at different times without HR overhead — and without having to fire someone when the strategy shifts.

3. A buyer doesn’t always want to inherit your team. Founders are taught that a strong team is an asset. For some acquirers, it is. For others, low overhead and absorbable revenue is more valuable. Lean is not a weakness in an exit conversation — it’s an option.

4. Off-the-shelf social listening tools are a starting point, not a finish line. Brandwatch and the rest answer the questions they were designed to answer. The questions a specific founder has about their category, channels, and reviews are different — and now buildable in an afternoon.

5. The custom-agent advantage compounds with data. Larken has seven years of customer reviews, survey responses, customer service emails, and channel-specific performance data. Pointing a custom agent at that corpus surfaces patterns no off-the-shelf tool can see, because no off-the-shelf tool has access to it.

6. AI is a redeployment lever, not a replacement lever. Katie’s demand planner used to be a buyer at Victoria’s Secret. Paying her to fill in spreadsheet formulas is a category error. The agent does the formulas. She does the judgment. The total cost stays the same. The output gets dramatically better.

7. Personal life is where most founders first unlock AI, before they do so at work. The shift from chatbot to autonomous agent clicked for Katie when she fed three school newsletters into Claude and got her family calendar populated automatically. The work use cases came after. If you haven’t had that moment yet, look for it in the messy, manual parts of your home life first.

8. You don’t need to be technical. You need to be willing to start. Katie is a former lawyer with no engineering background. Her builds happen in Base44 (vibe-coded super agents) and Claude. The barrier to entry isn’t skill anymore. It’s the decision to stop reading tool newsletters and to pick one workflow to automate.

9. A chief-of-staff agent is the highest-leverage first build for a founder. Email triage, meeting prep, calendar reconciliation, newsletter summarization — all running overnight, all surfaced as a single morning brief. It’s the closest thing to hiring an EA at a fraction of the cost, and it works as well for a founder’s family logistics as it does for the business.

10. Stop thinking in tools. Start thinking in workflows. Katie spent months testing fifteen different subscriptions before realizing the right question wasn’t “what tool does this.” It was “if no AI was involved in my day, where am I spending my time, and which of those pieces deserve human judgment?” The infrastructure follows from that question, not the other way around.

5 Things Worth a CMO’s Attention

1. The “no marketing team” model is no longer a constraint — it’s a strategy

Larken is a multi-million-dollar brand on the Inc. 5000 list with two part-time co-founders and zero full-time marketers. The work is carried out through an agency relationship that has matured into something closer to an embedded team, supplemented by contractors who are mostly customers themselves. This isn’t a story about being unable to afford headcount. It’s a deliberate structural choice that gives Katie access to a roster of specialists she could never have justified hiring individually.

So what: If you’re sitting on a marketing org chart with five mid-level generalists, ask whether you’d rebuild it the same way today. The agency-plus-contractor model used to be a step toward a “real” team. For lean B2C operators — and increasingly for B2B teams under cost pressure — it’s the destination, not the detour. The hidden cost in this model is the founder’s coordination time. The hidden benefit is access to talent you couldn’t otherwise afford.

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But if you read Stripe’s actual job description closely, it’s probably one of the clearest signals yet for where marketing work is heading. Not because of the AI buzzwords. Because the role is fundamentally not about campaigns, copy, media buying, or even strategy in the traditional sense.

It’s about changing how other marketers operate.

If you missed the news: Stripe is hiring someone to embed directly with marketing teams and make “AI the default mode.” Their success metrics are fascinating. Not leads generated, not pipeline, not MQLs. The role is judged by how many workflows become permanently transformed and how many colleagues start tasks with AI tools first.

That’s a management consultant. A systems architect. An internal transformation coach. A workflow designer. Maybe even a therapist for a…

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Yesterday it shipped.

The beta of Ads Manager is now officially open to U.S. advertisers: register, add payment, set budgets, upload ads, run campaigns, watch performance. CPC bidding is in alongside the existing CPM. Finally, there’s a Conversions API and a pixel for measuring whatever counts as a conversion in your business: purchases, sign-ups, lead forms. The previous $50K test-spend floor is gone too.

Registrations are manually approved for now, so we can’t walk you through the actual UI yet. That’ll come in a day or two, once our account clears.

Before you get excited, the obvious caveat: ChatGPT’s CTR is rough.

Independent estimates put it at around 1.3% versus Google Search’s 29.2%. Which makes sense if you think about it: people in a conversation aren’t trying to leave it. But Crite…

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Amman’s core argument: the AI edge in agency work isn’t a single clever agent. It’s a team of narrow agents with a brain on top — and a willingness to rewrite the client’s brief before you ever hit run. The agencies that still treat AI as a faster version of the old workflow are the ones losing pitches to those rebuilding the workflow from scratch.

About Amman Badlani

Amman Badlani has spent 22 years in digital media and commerce — across paid search, SEO, programmatic, retail media, and shopper marketing. He started on the client side at Philips Electronics, ran his own consultancy for a decade, serving SMBs, and then moved into the agency world 13 years ago. He has led practices at Omnicom, GroupM, and WPP Media, working with brands including Ford, Unilever, Bose, and Church & Dwight. He launched Meridian Commerce Partners, a venture focused on AI-native commerce orchestration. He is based in Brooklyn, New York.

You can find Amman on LinkedIn or email him at amman (at) meridiancommercepartners (dot) com

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About Peter Benei

co-founded the daily intelligence platform for senior marketing leaders. Peter has been serving as a CMO, marketing leader, and consultant to high-growth B2B scaleups for the past 10+ years. He has a background in advertising, working with Fortune 500 brands.

Connect with Peter on LinkedIn or read his newsletter.

Top 10 Takeaways

1. Adopting AI isn’t enough — the systems around it are the moat. “I’m using AI” was a credible answer in 2023. It isn’t anymore. The differentiator is the framework: how you structure agents, how data flows between them, and how outputs ladder back to a business question. Tools are a commodity. Workflow architecture is not.

2. The agency model is shifting from creative service to software. WPP and the rest aren’t pivoting because they want to — they’re pivoting because clients are demanding outcomes the old retainer-and-headcount model can’t deliver. The next decade of agency work looks more like productized orchestration than billable hours on a brief.

3. Black-box platform dashboards fail because clients still want to see inside. Google and Meta keep pitching the “trust the algorithm” model — give us the goal, we’ll handle the rest. Clients don’t buy it. They want transparency on where media shows up, what’s working, and what’s not. Any agentic system you build has to surface reasoning, not hide it.

4. No single platform sees the full picture, which is why orchestration is the opportunity. A CPG brand runs across TV, programmatic, retail media, search, shopper, influencer, and else. None of those silos sees the others. The agency that builds an agentic layer on top of all of them — pulling category sales, retailer performance, weather triggers, competitive moves into a single brain — does work that no platform can replicate.

5. The 20-agent team beats the one-agent generalist. Most marketers building with AI create a single agent that does too much. The right architecture is the inverse: many narrow specialists (category sales, retailer performance, search velocity, competitive moves) that talk to each other, with an orchestration brain on top translating the conversation into the language of a senior client.

6. Don’t push the brief down to every agent. Push it to the brain. If you narrow data collection at the agent level based on a half-baked client brief, you’ll miss the surprises that make the work valuable. Feed agents wide data; let the orchestration layer apply the brief. That’s how you find the insight the client didn’t know to ask for.

7. Briefs are starting points, not contracts. Clients often don’t know what they want — they know what they think they want. Your job isn’t to ignore the brief; it’s to rewrite it more precisely than they did, then go find the data that answers the question they actually have. The hand-holding is the value.

8. AI gives mid-market teams access to talent they couldn’t afford before. Big agencies used to staff Oxford-trained statisticians on the floor to find anomalies in performance data. Mid-market couldn’t. Now, an analyst persona inside an agentic system does the same pattern recognition at a fraction of the cost — and runs continuously, not in monthly cycles.

9. Hourly billing breaks the moment AI compresses the work. Updating 150 banner variants used to take a designer two hours. Now it takes minutes. If you’re billing the same way, you’re either lying or you’re losing the contract. The value shifts from execution time to hypothesis quality — what to test, what to read into the results, what to recommend next.

10. Build your own frameworks. Don’t just read other people’s. Amman reads eMarketer, Euromonitor, and YouGov — but treats them as inputs, not outputs. The actual practice is synthesizing what he reads into three or four personal frameworks he keeps refining as new information lands. The frameworks are the asset. The articles are the raw material.

5 Things Worth a CMO’s Attention

1. The agency model is bifurcating, and it’s happening faster than the org charts suggest

The WPP earnings story isn’t an isolated event — it’s the leading edge of a structural shift. The big networks are being asked to deliver software-grade orchestration on creative-service contracts, and the math doesn’t work. Smaller, more nimble agencies that build proprietary agentic stacks are starting to win pitches against firms ten times their size, because the work product is genuinely different.

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Which is why CEO Brian Niccol’s announcement this week reads like an admission. After trying for two years to make operations “lean”, Starbucks is hiring back thousands of baristas and pulling back its Siren System automation rollout. They tried to automate the part that wasn’t actually broken.

Yes, this is an automation story more than an AI one. But the lesson translates exactly, which is why we’re spending today on it.

If you missed the news: Starbucks spent two years rolling out the Siren System, a suite of equipment built to streamline drink-making and let stores run leaner. North American same-store sales fell 1% last quarter. Margins have shrunk five quarters running. Removing labor under the assumptio…

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Four companies that posted record earnings in the same quarter announced layoffs in the same month, citing AI in plain language. Oracle. Meta. Snap. Microsoft. Together, somewhere between forty and fifty thousand jobs, depending on which estimate of Oracle you trust. None of these companies was in trouble. Meta is spending up to $135 billion on AI infrastructure this year — nearly double last year — and removing 14,000 positions from the same plan to help fund it.

This is a substitution.

We spent the month watching the same pattern repeat in three different markets. Profitable companies turning headcount into AI capex. Software vendors are quietly becoming backends that AI calls. Search traffic moving into conversations that brands cannot show up in unless they are already cited.

The April report covers all three and is now live for paid members. This piece is the version for everyone else — long enough to be useful on its own, short enough to read on a Sunday morning.

We called the report The Substitution Quarter because that is what April did across labor, tools, and channels simultaneously.

The labor side.

The Writer / Workplace Intelligence survey landed on April 7, and the numbers do not require interpretation. Sixty percent of executives plan to lay off employees who refuse to adopt AI. Seventy-seven percent will not promote them. Twenty-nine percent of employees, meanwhile, admit they are actively sabotaging their company’s AI rollout — among Gen Z, that figure is forty-four percent. The same week, Andrew Ng coined the phrase “the marketing bottleneck” to describe the obvious mismatch between AI-native engineering teams shipping daily and marketing teams that still need three weeks and four meetings to publish the landing page.

We covered the org-design version of this in Marketing Is the New Bottleneck and the more uncomfortable version — Meta secretly logging keystrokes to train its agents — in The Way You Work IS Training Data For AI.

The structural reading is straightforward. The roles getting cut at Block, Snap, Meta, and Oracle are not random. They are concentrated in the layer of work that translates strategic direction into operational tasks and tracks progress between teams. That layer is what AI handles well. Senior judgment at the top is harder to automate. Pure execution at the bottom is harder to organize against.

The middle is where the substitution is happening, and this is where most current marketing director roles sit.

Spencer Stuart’s salary data still shows a 15–22% Director-level premium for documented AI deployment. April is the month when premium starts to compress — because the supply of leaders who can point to a deployed, measured workflow is about to grow fast.

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The tool side.

On April 27, Adobe made its entire Creative Cloud — fifty-plus pro-grade tools across Photoshop, Illustrator, Firefly, Premiere, Lightroom, InDesign, and more — available as a connector in Claude. The same week, Anthropic shipped the same announcement across Blender, Autodesk, Ableton, Splice, Affinity by Canva, SketchUp, and Resolume. You describe what you want. Claude decides which tool to use. The tool runs in the background.

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The summer RFS goes one layer deeper and deserves a new acronym because the old one no longer means what it used to.

For 20 years, SaaS meant Software-as-a-Service. You paid for a subscription. You got access to a tool. Your team did the work inside that tool.

YC just funded the inversion: Service-as-a-Software. You pay a subscription. You get the outcome. Nobody on your team does the work — because the service itself is now the product. Plug-and-play workforce is the endgame.

The summer RFS describes the three layers that make this possible:

Layer 1 — Company Brain. Extract tribal knowledge from people’s heads, Slack threads, tickets, and old emails. Structure it. Keep it current. Make it executable. Any startup that builds solutions for a company brain w…

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